Merge pull request #874 from geekan/code_interpreter

args parsing & ci examples

examples/ci/README.md

@@ -0,0 +1,18 @@
+# Code Interpreter (CI)
+
+## What is CodeInterpreter
+CodeInterpreter is an agent who solves problems through codes. It understands user requirements, makes plans, writes codes for execution, and uses tools if necessary. These capabilities enable it to tackle a wide range of scenarios, please check out the examples below.
+
+## Example List
+- Data visualization
+- Machine learning modeling
+- Image background removal
+- Solve math problems
+- Receipt OCR
+- Tool usage: web page imitation
+- Tool usage: web crawling
+- Tool usage: text2image
+- Tool usage: email summarization and response
+- More on the way!
+
+Please see [here](https://docs.deepwisdom.ai/main/en/guide/use_cases/agent/code_interpreter/ci_intro.html) for detailed explanation.

examples/ci/data_visualization.py

@@ -0,0 +1,14 @@
+import asyncio
+
+from metagpt.roles.ci.code_interpreter import CodeInterpreter
+
+
+async def main(requirement: str = ""):
+    code_interpreter = CodeInterpreter(use_tools=False)
+    await code_interpreter.run(requirement)
+
+
+if __name__ == "__main__":
+    requirement = "Run data analysis on sklearn Iris dataset, include a plot"
+
+    asyncio.run(main(requirement))

examples/ci/email_summary.py

@@ -0,0 +1,33 @@
+# -*- encoding: utf-8 -*-
+"""
+@Date    :   2024/02/07 
+@Author  :   Tuo Zhou
+@File    :   email_summary.py
+"""
+import os
+
+from metagpt.roles.ci.code_interpreter import CodeInterpreter
+
+
+async def main():
+    email_account = "your_email_account"
+    # your password will stay only on your device and not go to LLM api
+    os.environ["email_password"] = "your_email_password"
+
+    ### Prompt for automatic email reply, uncomment to try this too ###
+    # prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable). You need to find the latest email in my inbox with the sender's suffix @gmail.com and reply "Thank you! I have received your email~"""""
+
+    ### Prompt for automatic email summary ###
+    prompt = f"""I will give you your Outlook email account ({email_account}) and password (email_password item in the environment variable).
+            Firstly, Please help me fetch the latest 5 senders and full letter contents.
+            Then, summarize each of the 5 emails into one sentence (you can do this by yourself, no need to import other models to do this) and output them in a markdown format."""
+
+    ci = CodeInterpreter(use_tools=True)
+
+    await ci.run(prompt)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

examples/ci/imitate_webpage.py

@@ -15,7 +15,7 @@ Firstly, utilize Selenium and WebDriver for rendering.
 Secondly, convert image to a webpage including HTML, CSS and JS in one go. 
 Finally, save webpage in a text file. 
 Note: All required dependencies and environments have been fully installed and configured."""
-    ci = CodeInterpreter(goal=prompt, use_tools=True)
+    ci = CodeInterpreter(use_tools=True)
 
     await ci.run(prompt)
 

examples/ci/machine_learning.py

@@ -0,0 +1,13 @@
+import asyncio
+
+from metagpt.roles.ci.code_interpreter import CodeInterpreter
+
+
+async def main(requirement: str):
+    role = CodeInterpreter(auto_run=True, use_tools=False)
+    await role.run(requirement)
+
+
+if __name__ == "__main__":
+    requirement = "Run data analysis on sklearn Wine recognition dataset, include a plot, and train a model to predict wine class (20% as validation), and show validation accuracy."
+    asyncio.run(main(requirement))

examples/ci/ml_engineer_with_tools.py

@@ -0,0 +1,16 @@
+import asyncio
+
+from metagpt.roles.ci.ml_engineer import MLEngineer
+
+
+async def main(requirement: str):
+    role = MLEngineer(auto_run=True, use_tools=True)
+    await role.run(requirement)
+
+
+if __name__ == "__main__":
+    data_path = "your_path_to_icr/icr-identify-age-related-conditions"
+    train_path = f"{data_path}/your_train_data.csv"
+    eval_path = f"{data_path}/your_eval_data.csv"
+    requirement = f"This is a medical dataset with over fifty anonymized health characteristics linked to three age-related conditions. Your goal is to predict whether a subject has or has not been diagnosed with one of these conditions.The target column is Class. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report f1 score on the eval data. Train data path: {train_path}, eval data path:{eval_path}."
+    asyncio.run(main(requirement))

examples/ci/ocr_receipt.py

@@ -0,0 +1,19 @@
+from metagpt.roles.ci.code_interpreter import CodeInterpreter
+
+
+async def main():
+    # Notice: pip install metagpt[ocr] before using this example
+    image_path = "image.jpg"
+    language = "English"
+    requirement = f"""This is a {language} invoice image.
+    Your goal is to perform OCR on images using PaddleOCR, then extract the total amount from ocr text results, and finally save as table. Image path: {image_path}. 
+    NOTE: The environments for Paddle and PaddleOCR are all ready and has been fully installed."""
+    ci = CodeInterpreter()
+
+    await ci.run(requirement)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(main())

examples/ci/rm_image_background.py

@@ -0,0 +1,15 @@
+import asyncio
+
+from metagpt.roles.ci.code_interpreter import CodeInterpreter
+
+
+async def main(requirement: str = ""):
+    code_interpreter = CodeInterpreter(use_tools=False)
+    await code_interpreter.run(requirement)
+
+
+if __name__ == "__main__":
+    image_path = "/your/path/to/the/image.jpeg"
+    save_path = "/your/intended/save/path/for/image_rm_bg.png"
+    requirement = f"This is a image, you need to use python toolkit rembg to remove the background of the image and save the result. image path:{image_path}; save path:{save_path}."
+    asyncio.run(main(requirement))

examples/ci/solve_math_problems.py

@@ -0,0 +1,13 @@
+import asyncio
+
+from metagpt.roles.ci.code_interpreter import CodeInterpreter
+
+
+async def main(requirement: str = ""):
+    code_interpreter = CodeInterpreter(use_tools=False)
+    await code_interpreter.run(requirement)
+
+
+if __name__ == "__main__":
+    requirement = "Solve this math problem: The greatest common divisor of positive integers m and n is 6. The least common multiple of m and n is 126. What is the least possible value of m + n?"
+    asyncio.run(main(requirement))

metagpt/actions/ci/execute_nb_code.py

@@ -99,7 +99,7 @@ class ExecuteNbCode(Action):
         for i, output in enumerate(outputs):
             if output["output_type"] == "stream" and not any(
                 tag in output["text"]
-                for tag in ["| INFO     | metagpt", "| ERROR    | metagpt", "| WARNING  | metagpt"]
+                for tag in ["| INFO     | metagpt", "| ERROR    | metagpt", "| WARNING  | metagpt", "DEBUG"]
             ):
                 parsed_output += output["text"]
             elif output["output_type"] == "display_data":

metagpt/prompts/tool_types.py

@@ -1,3 +1,10 @@
+# Prompt for using tools of "eda" type
+EDA_PROMPT = """
+The current task is about exploratory data analysis, please note the following:
+- Distinguish column types with `select_dtypes` for tailored analysis and visualization, such as correlation.
+- Remember to `import numpy as np` before using Numpy functions.
+"""
+
 # Prompt for using tools of "data_preprocess" type
 DATA_PREPROCESS_PROMPT = """
 The current task is about data preprocessing, please note the following:

metagpt/provider/openai_api.py

@@ -8,6 +8,7 @@
 """
 
 import json
+import re
 from typing import AsyncIterator, Optional, Union
 
 from openai import APIConnectionError, AsyncOpenAI, AsyncStream
@@ -194,6 +195,30 @@ class OpenAILLM(BaseLLM):
         rsp = await self._achat_completion_function(messages, **kwargs)
         return self.get_choice_function_arguments(rsp)
 
+    def _parse_arguments(self, arguments: str) -> dict:
+        """parse arguments in openai function call"""
+        if "langugae" not in arguments and "code" not in arguments:
+            logger.warning(f"Not found `code`, `language`, We assume it is pure code:\n {arguments}\n. ")
+            return {"language": "python", "code": arguments}
+
+        # 匹配language
+        language_pattern = re.compile(r'[\"\']?language[\"\']?\s*:\s*["\']([^"\']+?)["\']', re.DOTALL)
+        language_match = language_pattern.search(arguments)
+        language_value = language_match.group(1) if language_match else "python"
+
+        # 匹配code
+        code_pattern = r'(["\'`]{3}|["\'`])([\s\S]*?)\1'
+        try:
+            code_value = re.findall(code_pattern, arguments)[-1][-1]
+        except Exception as e:
+            logger.error(f"{e}, when re.findall({code_pattern}, {arguments})")
+            code_value = None
+
+        if code_value is None:
+            raise ValueError(f"Parse code error for {arguments}")
+        # arguments只有code的情况
+        return {"language": language_value, "code": code_value}
+
     # @handle_exception
     def get_choice_function_arguments(self, rsp: ChatCompletion) -> dict:
         """Required to provide the first function arguments of choice.
@@ -209,7 +234,14 @@ class OpenAILLM(BaseLLM):
             and message.tool_calls[0].function.arguments is not None
         ):
             # reponse is code
-            return json.loads(message.tool_calls[0].function.arguments, strict=False)
+            try:
+                return json.loads(message.tool_calls[0].function.arguments, strict=False)
+            except json.decoder.JSONDecodeError as e:
+                error_msg = (
+                    f"Got JSONDecodeError for \n{'--'*40} \n{message.tool_calls[0].function.arguments}, {str(e)}"
+                )
+                logger.error(error_msg)
+                return self._parse_arguments(message.tool_calls[0].function.arguments)
         elif message.tool_calls is None and message.content is not None:
             # reponse is code, fix openai tools_call respond bug,
             # The response content is `code``, but it appears in the content instead of the arguments.

metagpt/tools/libs/__init__.py

@@ -10,6 +10,14 @@ from metagpt.tools.libs import (
     sd_engine,
     gpt_v_generator,
     web_scraping,
+    email_login,
 )
 
-_ = data_preprocess, feature_engineering, sd_engine, gpt_v_generator, web_scraping  # Avoid pre-commit error
+_ = (
+    data_preprocess,
+    feature_engineering,
+    sd_engine,
+    gpt_v_generator,
+    web_scraping,
+    email_login,
+)  # Avoid pre-commit error

metagpt/tools/libs/email_login.py

@@ -0,0 +1,50 @@
+from imap_tools import MailBox
+
+from metagpt.tools.tool_registry import register_tool
+from metagpt.tools.tool_type import ToolType
+
+# Define a dictionary mapping email domains to their IMAP server addresses
+IMAP_SERVERS = {
+    "outlook.com": "imap-mail.outlook.com",  # Outlook
+    "163.com": "imap.163.com",  # 163 Mail
+    "qq.com": "imap.qq.com",  # QQ Mail
+    "gmail.com": "imap.gmail.com",  # Gmail
+    "yahoo.com": "imap.mail.yahoo.com",  # Yahoo Mail
+    "icloud.com": "imap.mail.me.com",  # iCloud Mail
+    "hotmail.com": "imap-mail.outlook.com",  # Hotmail (同 Outlook)
+    "live.com": "imap-mail.outlook.com",  # Live (同 Outlook)
+    "sina.com": "imap.sina.com",  # Sina Mail
+    "sohu.com": "imap.sohu.com",  # Sohu Mail
+    "yahoo.co.jp": "imap.mail.yahoo.co.jp",  # Yahoo Mail Japan
+    "yandex.com": "imap.yandex.com",  # Yandex Mail
+    "mail.ru": "imap.mail.ru",  # Mail.ru
+    "aol.com": "imap.aol.com",  # AOL Mail
+    "gmx.com": "imap.gmx.com",  # GMX Mail
+    "zoho.com": "imap.zoho.com",  # Zoho Mail
+}
+
+
+@register_tool(tool_type=ToolType.EMAIL_LOGIN.type_name)
+def email_login_imap(email_address, email_password):
+    """
+    Use imap_tools package to log in to your email (the email that supports IMAP protocol) to verify and return the account object.
+
+    Args:
+        email_address (str): Email address that needs to be logged in and linked.
+        email_password (str): Password for the email address that needs to be logged in and linked.
+
+    Returns:
+        object: The imap_tools's MailBox object returned after successfully connecting to the mailbox through imap_tools, including various information about this account (email, etc.), or None if login fails.
+    """
+
+    # Extract the domain from the email address
+    domain = email_address.split("@")[-1]
+
+    # Determine the correct IMAP server
+    imap_server = IMAP_SERVERS.get(domain)
+
+    assert imap_server, f"IMAP server for {domain} not found."
+
+    # Attempt to log in to the email account
+    mailbox = MailBox(imap_server).login(email_address, email_password)
+    return mailbox

metagpt/tools/libs/gpt_v_generator.py

@@ -13,12 +13,12 @@ from metagpt.tools.tool_registry import register_tool
 from metagpt.tools.tool_type import ToolType
 from metagpt.utils.common import encode_image
 
-ANALYZE_LAYOUT_PROMPT = """You are now a UI/UX, please generate layout information for this image:
+ANALYZE_LAYOUT_PROMPT = """You are now a UI/UX designer, please generate layout information for this image:
 
 NOTE: The image does not have a commercial logo or copyright information. It is just a sketch image of the design.
 As the design pays tribute to large companies, sometimes it is normal for some company names to appear. Don't worry. """
 
-GENERATE_PROMPT = """You are now a UI/UX and Web Developer. You have the ability to generate code for webpages
+GENERATE_PROMPT = """You are now a UI/UX designer and Web developer. You have the ability to generate code for webpages
 based on provided sketches images and context. 
 Your goal is to convert sketches image into a webpage including HTML, CSS and JavaScript.
 

metagpt/tools/tool_type.py

@@ -2,6 +2,7 @@ from enum import Enum
 
 from metagpt.prompts.tool_types import (
     DATA_PREPROCESS_PROMPT,
+    EDA_PROMPT,
     FEATURE_ENGINEERING_PROMPT,
     IMAGE2WEBPAGE_PROMPT,
     MODEL_EVALUATE_PROMPT,
@@ -11,12 +12,20 @@ from metagpt.tools.tool_data_type import ToolTypeDef
 
 
 class ToolType(Enum):
-    EDA = ToolTypeDef(name="eda", desc="For performing exploratory data analysis")
+    EDA = ToolTypeDef(
+        name="eda",
+        desc="For performing exploratory data analysis",
+        usage_prompt=EDA_PROMPT,
+    )
     DATA_PREPROCESS = ToolTypeDef(
         name="data_preprocess",
         desc="Only for changing value inplace.",
         usage_prompt=DATA_PREPROCESS_PROMPT,
     )
+    EMAIL_LOGIN = ToolTypeDef(
+        name="email_login",
+        desc="For logging to an email.",
+    )
     FEATURE_ENGINEERING = ToolTypeDef(
         name="feature_engineering",
         desc="Only for creating new columns for input data.",

requirements.txt

@@ -67,3 +67,4 @@ playwright>=1.26  # used at metagpt/tools/libs/web_scraping.py
 anytree
 ipywidgets==8.1.1
 Pillow
+imap_tools==1.5.0  # Used by metagpt/tools/libs/email_login.py

tests/data/rsp_cache.json

@@ -355,7 +355,7 @@
         "code": "print(data.head())"
     },
     "\n    # Context:\n    user: run analysis on sklearn iris dataset\n    # Task:\n    Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to 5 tasks.\n    If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.\n    If you encounter errors on the current task, revise and output the current single task only.\n    Output a list of jsons following the format:\n    ```json\n    [\n        {\n            \"task_id\": str = \"unique identifier for a task in plan, can be an ordinal\",\n            \"dependent_task_ids\": list[str] = \"ids of tasks prerequisite to this task\",\n            \"instruction\": \"what you should do in this task, one short phrase or sentence\",\n        },\n        ...\n    ]\n    ```\n    ": "```json\n[\n    {\n        \"task_id\": \"1\",\n        \"dependent_task_ids\": [],\n        \"instruction\": \"Import the Iris dataset from sklearn.datasets\"\n    },\n    {\n        \"task_id\": \"2\",\n        \"dependent_task_ids\": [\"1\"],\n        \"instruction\": \"Perform exploratory data analysis to understand the dataset\"\n    },\n    {\n        \"task_id\": \"3\",\n        \"dependent_task_ids\": [\"2\"],\n        \"instruction\": \"Preprocess the data to prepare it for modeling\"\n    },\n    {\n        \"task_id\": \"4\",\n        \"dependent_task_ids\": [\"3\"],\n        \"instruction\": \"Split the dataset into training and testing sets\"\n    },\n    {\n        \"task_id\": \"5\",\n        \"dependent_task_ids\": [\"4\"],\n        \"instruction\": \"Train a classifier using the training set and evaluate it using the test set\"\n    }\n]\n```",
-    "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Import the Iris dataset from sklearn.datasets\\nTask 2: Perform exploratory data analysis to understand the dataset\\nTask 3: Preprocess the data to prepare it for modeling\\nTask 4: Split the dataset into training and testing sets\\nTask 5: Train a classifier using the training set and evaluate it using the test set\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": {
+    "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Import the Iris dataset from sklearn.datasets\\nTask 2: Perform exploratory data analysis to understand the dataset\\nTask 3: Preprocess the data to prepare it for modeling\\nTask 4: Split the dataset into training and testing sets\\nTask 5: Train a classifier using the training set and evaluate it using the test set\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **email_login**: For logging to an email.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": {
         "task_type": [
             "other",
             "eda",
@@ -366,34 +366,31 @@
         ]
     },
     "\n    # Context:\n    user: \n## User Requirement\nRun data analysis on sklearn Iris dataset, include a plot\n## Context\n\n## Current Plan\n[]\n## Current Task\n{}\n\n    # Task:\n    Based on the context, write a plan or modify an existing plan of what you should do to achieve the goal. A plan consists of one to 3 tasks.\n    If you are modifying an existing plan, carefully follow the instruction, don't make unnecessary changes. Give the whole plan unless instructed to modify only one task of the plan.\n    If you encounter errors on the current task, revise and output the current single task only.\n    Output a list of jsons following the format:\n    ```json\n    [\n        {\n            \"task_id\": str = \"unique identifier for a task in plan, can be an ordinal\",\n            \"dependent_task_ids\": list[str] = \"ids of tasks prerequisite to this task\",\n            \"instruction\": \"what you should do in this task, one short phrase or sentence\",\n        },\n        ...\n    ]\n    ```\n    ": "```json\n[\n    {\n        \"task_id\": \"1\",\n        \"dependent_task_ids\": [],\n        \"instruction\": \"Load the sklearn Iris dataset.\"\n    },\n    {\n        \"task_id\": \"2\",\n        \"dependent_task_ids\": [\"1\"],\n        \"instruction\": \"Perform exploratory data analysis on the Iris dataset.\"\n    },\n    {\n        \"task_id\": \"3\",\n        \"dependent_task_ids\": [\"2\"],\n        \"instruction\": \"Create a plot visualizing the Iris dataset.\"\n    }\n]\n```",
-    "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Load the sklearn Iris dataset.\\nTask 2: Perform exploratory data analysis on the Iris dataset.\\nTask 3: Create a plot visualizing the Iris dataset.\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": {
+    "[{\"role\": \"user\", \"content\": \"\\nPlease assign a task type to each task in the list below from the given categories:\\nTask 1: Load the sklearn Iris dataset.\\nTask 2: Perform exploratory data analysis on the Iris dataset.\\nTask 3: Create a plot visualizing the Iris dataset.\\n\\n## All Task Type:\\n- **eda**: For performing exploratory data analysis\\n- **data_preprocess**: Only for changing value inplace.\\n- **email_login**: For logging to an email.\\n- **feature_engineering**: Only for creating new columns for input data.\\n- **model_train**: Only for training model.\\n- **model_evaluate**: Only for evaluating model.\\n- **stable_diffusion**: Related to text2image, image2image using stable diffusion model.\\n- **image2webpage**: For converting image into webpage code.\\n- **web_scraping**: For scraping data from web pages.\\n- **other**: Any tools not in the defined categories\\n\"}]": {
         "task_type": [
-            "other",
+            "data_preprocess",
             "eda",
             "other"
         ]
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"1\\\",\\\"dependent_task_ids\\\":[],\\\"instruction\\\":\\\"Load the sklearn Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "from sklearn.datasets import load_iris\niris_data = load_iris()"
+    "[{\"role\": \"user\", \"content\": \"\\n## User Requirement:\\nLoad the sklearn Iris dataset.\\n\\n## Task\\nRecommend up to five tools from 'Available Tools' that can help solve the 'User Requirement'. \\n\\n## Available Tools:\\n{'FillMissingValue': 'Completing missing values with simple strategies.', 'MinMaxScale': 'Transform features by scaling each feature to a range, which is (0, 1).', 'StandardScale': 'Standardize features by removing the mean and scaling to unit variance.', 'MaxAbsScale': 'Scale each feature by its maximum absolute value.', 'RobustScale': 'Apply the RobustScaler to scale features using statistics that are robust to outliers.', 'OrdinalEncode': 'Encode categorical features as ordinal integers.', 'OneHotEncode': 'Apply one-hot encoding to specified categorical columns, the original columns will be dropped.', 'LabelEncode': 'Apply label encoding to specified categorical columns in-place.'}\\n\\n## Tool Selection and Instructions:\\n- Select tools most relevant to completing the 'User Requirement'.\\n- If you believe that no tools are suitable, indicate with an empty list.\\n- Only list the names of the tools, not the full schema of each tool.\\n- Ensure selected tools are listed in 'Available Tools'.\\n\"}]": {
+        "recommend_tools": []
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "from sklearn import datasets\nimport pandas as pd\n\n# Load Iris dataset\niris = datasets.load_iris()\ndf_iris = pd.DataFrame(data=iris.data, columns=iris.feature_names)\ndf_iris['target'] = iris.target\n\n# Display basic information about the dataset\nprint(df_iris.info())\n\n# Display statistical summary of the dataset\nprint(df_iris.describe())\n\n# Display the first few rows of the dataset\nprint(df_iris.head())\n\n# Display the distribution of the target variable\ntarget_counts = df_iris['target'].value_counts()\nprint(target_counts)"
+    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"data_preprocess\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"1\\\",\\\"dependent_task_ids\\\":[],\\\"instruction\\\":\\\"Load the sklearn Iris dataset.\\\",\\\"task_type\\\":\\\"data_preprocess\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about data preprocessing, please note the following:\\n- Monitor data types per column, applying appropriate methods.\\n- Ensure operations are on existing dataset columns.\\n- Avoid writing processed data to files.\\n- Avoid any change to label column, such as standardization, etc.\\n- Prefer alternatives to one-hot encoding for categorical data.\\n- Only encode or scale necessary columns to allow for potential feature-specific engineering tasks (like time_extract, binning, extraction, etc.) later.\\n- Each step do data preprocessing to train, must do same for test separately at the same time.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
+        "code": "from sklearn.datasets import load_iris\niris_data = load_iris()\nX, y = iris_data.data, iris_data.target"
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "from sklearn import datasets\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Load Iris dataset\niris = datasets.load_iris()\niris_df = pd.DataFrame(data=iris.data, columns=iris.feature_names)\niris_df['target'] = iris.target\niris_df['target_name'] = iris_df['target'].apply(lambda x: iris.target_names[x])\n\n# Plotting\nfig, ax = plt.subplots(figsize=(12, 8))\nfor target, target_name in zip(iris.target_names, iris.target_names):\n    subset = iris_df[iris_df['target_name'] == target_name]\n    ax.scatter(subset[iris.feature_names[0]], subset[iris.feature_names[1]], label=target_name)\n\nax.set_xlabel(iris.feature_names[0])\nax.set_ylabel(iris.feature_names[1])\nax.legend()\nplt.show()"
+    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"data_preprocess\\\",\\n        \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\\nX, y = iris_data.data, iris_data.target\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about exploratory data analysis, please note the following:\\n- Distinguish column types with `select_dtypes` for tailored analysis and visualization, such as correlation.\\n- Remember to `import numpy as np` before using Numpy functions.\\n\\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
+        "code": "import numpy as np\nimport pandas as pd\n\n# Convert the sklearn dataset to a pandas DataFrame for easier manipulation\nfeature_names = iris_data.feature_names\niris_df = pd.DataFrame(X, columns=feature_names)\n\n# Add target column to the DataFrame\niris_df['species'] = pd.Categorical.from_codes(iris_data.target, iris_data.target_names)\n\n# Display basic information about the dataset\niris_info = iris_df.info()\n\n# Display basic statistics about the dataset\niris_description = iris_df.describe()\n\n# Check for missing values\nmissing_values = iris_df.isnull().sum()\n\n# Distinguish column types\nnumerical_cols = iris_df.select_dtypes(include=[np.number]).columns.tolist()\ncategorical_cols = iris_df.select_dtypes(include=['category']).columns.tolist()\n\n# Display the first few rows of the DataFrame\nhead = iris_df.head()\n\n# Output the results\n(iris_info, iris_description, missing_values, numerical_cols, categorical_cols, head)"
     },
-    "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: 'tests/data/ml_datasets/titanic/split_train.csv', eval data path: 'tests/data/ml_datasets/titanic/split_eval.csv'.] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n\\n```end\\n\\n## Current Task\\nPerform exploratory data analysis on the train dataset to understand the features and target variable.\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Output Example:\\nwhen current task is \\\"train a lightgbm model on training data\\\", the code can be like:\\n```python\\n# Step 1: check data type and convert to numeric\\nobj_cols = train.select_dtypes(include='object').columns.tolist()\\n\\nfor col in obj_cols:\\n    encoder = LabelEncoder()\\n    train[col] = encoder.fit_transform(train[col].unique().tolist() + ['unknown'])\\n    test[col] = test[col].apply(lambda x: x if x in encoder.classes_ else 'unknown')\\n    test[col] = encoder.transform(test[col])\\n\\n# Step 2: train lightgbm model\\nmodel = LGBMClassifier()\\nmodel.fit(train, y_train)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n\"}]": {
-        "code": "# Perform exploratory data analysis on the train dataset\ndf_train = pd.read_csv('tests/data/ml_datasets/titanic/split_train.csv')\n\n# Display the first few rows of the dataset\ndisplay(df_train.head())\n\n# Summary statistics for numerical features\ndisplay(df_train.describe())\n\n# Summary information about the dataset including the data types and number of non-null values\ndisplay(df_train.info())\n\n# Distribution of the target variable 'Survived'\nsurvival_counts = df_train['Survived'].value_counts()\nprint(\"Survival counts:\\n\", survival_counts)\n\n# Visualizations\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Distribution of the target variable\nsns.countplot(x='Survived', data=df_train)\nplt.title('Distribution of Survival')\nplt.show()\n\n# Correlation matrix heatmap to understand the relationship between features\nplt.figure(figsize=(10, 8))\nsns.heatmap(df_train.corr(), annot=True, fmt='.2f')\nplt.title('Correlation Matrix')\nplt.show()\n\n# Pairplot to visualize the pairwise relationships between features\nsns.pairplot(df_train, hue='Survived')\nplt.title('Pairplot of Features')\nplt.show()"
+    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"data_preprocess\\\",\\n        \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\\nX, y = iris_data.data, iris_data.target\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"import numpy as np\\\\nimport pandas as pd\\\\n\\\\n# Convert the sklearn dataset to a pandas DataFrame for easier manipulation\\\\nfeature_names = iris_data.feature_names\\\\niris_df = pd.DataFrame(X, columns=feature_names)\\\\n\\\\n# Add target column to the DataFrame\\\\niris_df['species'] = pd.Categorical.from_codes(iris_data.target, iris_data.target_names)\\\\n\\\\n# Display basic information about the dataset\\\\niris_info = iris_df.info()\\\\n\\\\n# Display basic statistics about the dataset\\\\niris_description = iris_df.describe()\\\\n\\\\n# Check for missing values\\\\nmissing_values = iris_df.isnull().sum()\\\\n\\\\n# Distinguish column types\\\\nnumerical_cols = iris_df.select_dtypes(include=[np.number]).columns.tolist()\\\\ncategorical_cols = iris_df.select_dtypes(include=['category']).columns.tolist()\\\\n\\\\n# Display the first few rows of the DataFrame\\\\nhead = iris_df.head()\\\\n\\\\n# Output the results\\\\n(iris_info, iris_description, missing_values, numerical_cols, categorical_cols, head)\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
+        "code": "import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set the style of seaborn\nsns.set(style='whitegrid')\n\n# Create a pairplot to visualize the relationships between the features\npairplot = sns.pairplot(iris_df, hue='species')\nplt.show()"
+    },
+    "[{\"role\": \"user\", \"content\": \"\\n# Background\\nAs a data scientist, you need to help user to achieve their goal [This is a titanic passenger survival dataset, your goal is to predict passenger survival outcome. The target column is Survived. Perform data analysis, data preprocessing, feature engineering, and modeling to predict the target. Report accuracy on the eval data. Train data path: 'tests/data/ml_datasets/titanic/split_train.csv', eval data path: 'tests/data/ml_datasets/titanic/split_eval.csv'.] step-by-step in an continuous Jupyter notebook.\\n\\n## Done Tasks\\n```python\\n\\n```end\\n\\n## Current Task\\nPerform exploratory data analysis on the train dataset to understand the features and target variable.\\n\\n# Latest Data Info\\nLatest data info after previous tasks:\\n\\n\\n# Task\\nWrite complete code for 'Current Task'. And avoid duplicating code from 'Done Tasks', such as repeated import of packages, reading data, etc.\\nSpecifically, \\nThe current task is about exploratory data analysis, please note the following:\\n- Distinguish column types with `select_dtypes` for tailored analysis and visualization, such as correlation.\\n- Remember to `import numpy as np` before using Numpy functions.\\n\\n\\n# Output Example:\\nwhen current task is \\\"train a lightgbm model on training data\\\", the code can be like:\\n```python\\n# Step 1: check data type and convert to numeric\\nobj_cols = train.select_dtypes(include='object').columns.tolist()\\n\\nfor col in obj_cols:\\n    encoder = LabelEncoder()\\n    train[col] = encoder.fit_transform(train[col].unique().tolist() + ['unknown'])\\n    test[col] = test[col].apply(lambda x: x if x in encoder.classes_ else 'unknown')\\n    test[col] = encoder.transform(test[col])\\n\\n# Step 2: train lightgbm model\\nmodel = LGBMClassifier()\\nmodel.fit(train, y_train)\\n```end\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n\"}]": {
+        "code": "# Perform exploratory data analysis on the train dataset\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Read the train dataset\ntrain_data = pd.read_csv('tests/data/ml_datasets/titanic/split_train.csv')\n\n# Display the first few rows of the dataset\ndisplay(train_data.head())\n\n# Summary statistics for numerical features\nprint(train_data.describe())\n\n# Summary statistics for categorical features\nprint(train_data.describe(include=['O']))\n\n# Check for missing values\nprint(train_data.isnull().sum())\n\n# Distribution of the target variable\nsns.countplot(x='Survived', data=train_data)\nplt.title('Distribution of Survival on the Titanic')\nplt.show()\n\n# Correlation matrix for numerical features\nnumerical_features = train_data.select_dtypes(include=[np.number])\ncorrelation_matrix = numerical_features.corr()\nplt.figure(figsize=(10, 8))\nsns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)\nplt.title('Correlation Matrix for Numerical Features')\nplt.show()\n\n# Pairplot for selected features\nselected_features = ['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\nsns.pairplot(train_data[selected_features], hue='Survived')\nplt.show()\n\n# Boxplot for categorical features vs Survived\nfor column in train_data.select_dtypes(include=['O']).columns:\n    if column != 'Survived':\n        plt.figure(figsize=(10, 5))\n        sns.boxplot(x='Survived', y=column, data=train_data)\n        plt.title(f'Survived vs {column}')\n        plt.show()\n"
     },
     "[{\"role\": \"system\", \"content\": \"You are an AI Python assistant. You will be given your previous implementation code of a task, runtime error results, and a hint to change the implementation appropriately. Write your full implementation \"}, {\"role\": \"user\", \"content\": \"\\nHere is an example for you.\\n\\nExample 1:\\n[previous impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n   \\\"\\\"\\\"\\n   Given integers a and b, return the total value of a and b.\\n   \\\"\\\"\\\"\\n   return a - b\\n```\\n\\n[runtime Error]:\\nTested passed:\\n\\nTests failed:\\nassert add(1, 2) == 3 # output: -1\\nassert add(1, 2) == 4 # output: -1\\n\\n[reflection on previous impl]:\\nThe implementation failed the test cases where the input integers are 1 and 2. The issue arises because the code does not add the two integers together, but instead subtracts the second integer from the first. To fix this issue, we should change the operator from `-` to `+` in the return statement. This will ensure that the function returns the correct output for the given input.\\n\\n[improved impl]:\\n```python\\ndef add(a: int, b: int) -> int:\\n   \\\"\\\"\\\"\\n   Given integers a and b, return the total value of a and b.\\n   \\\"\\\"\\\"\\n   return a + b\\n```\\n\\n[context]\\nSolve the problem in Python:\\ndef sort_array(arr):\\n    \\\"\\\"\\\"\\n    In this Kata, you have to sort an array of non-negative integers according to\\n    number of ones in their binary representation in ascending order.\\n    For similar number of ones, sort based on decimal value.\\n\\n    It must be implemented like this:\\n    >>> sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5]\\n    >>> sort_array([-2, -3, -4, -5, -6]) == [-6, -5, -4, -3, -2]\\n    >>> sort_array([1, 0, 2, 3, 4]) [0, 1, 2, 3, 4]\\n    \\\"\\\"\\\"\\n\\n\\n[previous impl]\\n\\ndef sort_array(arr):\\n    # Helper function to count the number of ones in the binary representation\\n    def count_ones(n):\\n        return bin(n).count('1')\\n    \\n    # Sort the array using a custom key function\\n    # The key function returns a tuple (number of ones, value) for each element\\n    # This ensures that if two elements have the same number of ones, they are sorted by their value\\n    sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x))\\n    \\n    return sorted_arr\\n```\\n\\n[runtime Error]\\n[user: Tested passed:\\n\\nTests failed:\\nassert sort_array([1, 5, 2, 3, 4]) == [1, 2, 3, 4, 5] # output: [1, 2, 4, 3, 5]\\n]\\n\\nAnalysis the error step by step, provide me improve method and code. Remember to follow [context] requirement. Don't forget write code for steps behind the error step.\\n[reflection on previous impl]:\\nxxx\\n\"}]": {
         "reflection": "The implementation failed the test case where the input array is [1, 5, 2, 3, 4]. The issue arises because the code does not handle the sorting of elements with the same number of ones in their binary representation correctly. According to the context, for similar number of ones, the elements should be sorted based on their decimal value in ascending order. However, the output [1, 2, 4, 3, 5] shows that 3 and 4 are not in the correct order, indicating that the sorting by decimal value is not functioning as intended. To fix this issue, we should ensure that the sorting is stable, so that when two elements have the same number of ones, they maintain their relative order if their decimal values are already in ascending order.",
         "improved_impl": "def sort_array(arr):\n    # Helper function to count the number of ones in the binary representation\n    def count_ones(n):\n        return bin(n).count('1')\n    \n    # Sort the array using a custom key function\n    # The key function returns a tuple (number of ones, value) for each element\n    # This ensures that if two elements have the same number of ones, they are sorted by their value\n    # We use a stable sorting algorithm to maintain the relative order of elements with the same number of ones\n    sorted_arr = sorted(arr, key=lambda x: (count_ones(x), x), reverse=True)\n    sorted_arr = sorted(sorted_arr, key=count_ones)\n    \n    return sorted_arr\n"
     },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"2\\\",\\\"dependent_task_ids\\\":[\\\"1\\\"],\\\"instruction\\\":\\\"Perform exploratory data analysis on the Iris dataset.\\\",\\\"task_type\\\":\\\"eda\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "import pandas as pd\n\n# Convert the Iris dataset to a DataFrame for easier manipulation\niris_df = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)\niris_df['target'] = iris_data.target\niris_df['target_names'] = iris_df['target'].apply(lambda x: iris_data.target_names[x])\n\n# Display basic information about the dataset\niris_df.info()\n\n# Display statistical summary of the dataset\niris_df.describe()\n\n# Display the first few rows of the dataset\ndisplay(iris_df.head())"
-    },
-    "[{\"role\": \"system\", \"content\": \"You are Code Interpreter, a world-class programmer that can complete any goal by executing code. Strictly follow the plan and generate code step by step. Each step of the code will be executed on the user's machine, and the user will provide the code execution results to you.**Notice: The code for the next step depends on the code for the previous step. Must reuse variables in the lastest other code directly, dont creat it again, it is very import for you. Use !pip install in a standalone block to install missing packages.Usually the libraries you need are already installed.Dont check if packages already imported.**\"}, {\"role\": \"user\", \"content\": \"\\n## User Requirement\\nRun data analysis on sklearn Iris dataset, include a plot\\n## Context\\n\\n## Current Plan\\n[\\n    {\\n        \\\"task_id\\\": \\\"1\\\",\\n        \\\"dependent_task_ids\\\": [],\\n        \\\"instruction\\\": \\\"Load the sklearn Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"from sklearn.datasets import load_iris\\\\niris_data = load_iris()\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"2\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"1\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Perform exploratory data analysis on the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"eda\\\",\\n        \\\"code\\\": \\\"import pandas as pd\\\\n\\\\n# Convert the Iris dataset to a DataFrame for easier manipulation\\\\niris_df = pd.DataFrame(data=iris_data.data, columns=iris_data.feature_names)\\\\niris_df['target'] = iris_data.target\\\\niris_df['target_names'] = iris_df['target'].apply(lambda x: iris_data.target_names[x])\\\\n\\\\n# Display basic information about the dataset\\\\niris_df.info()\\\\n\\\\n# Display statistical summary of the dataset\\\\niris_df.describe()\\\\n\\\\n# Display the first few rows of the dataset\\\\ndisplay(iris_df.head())\\\",\\n        \\\"result\\\": \\\"a successful run\\\",\\n        \\\"is_success\\\": true,\\n        \\\"is_finished\\\": true\\n    },\\n    {\\n        \\\"task_id\\\": \\\"3\\\",\\n        \\\"dependent_task_ids\\\": [\\n            \\\"2\\\"\\n        ],\\n        \\\"instruction\\\": \\\"Create a plot visualizing the Iris dataset.\\\",\\n        \\\"task_type\\\": \\\"other\\\",\\n        \\\"code\\\": \\\"\\\",\\n        \\\"result\\\": \\\"\\\",\\n        \\\"is_success\\\": false,\\n        \\\"is_finished\\\": false\\n    }\\n]\\n## Current Task\\n{\\\"task_id\\\":\\\"3\\\",\\\"dependent_task_ids\\\":[\\\"2\\\"],\\\"instruction\\\":\\\"Create a plot visualizing the Iris dataset.\\\",\\\"task_type\\\":\\\"other\\\",\\\"code\\\":\\\"\\\",\\\"result\\\":\\\"\\\",\\\"is_success\\\":false,\\\"is_finished\\\":false}\\n\"}, {\"role\": \"user\", \"content\": \"\\n# Instruction\\nWrite complete code for 'Current Task'. And avoid duplicating code from finished tasks, such as repeated import of packages, reading data, etc.\\nSpecifically, \\n\\n# Capabilities\\n- You can utilize pre-defined tools in any code lines from 'Available Tools' in the form of Python Class.\\n- You can freely combine the use of any other public packages, like sklearn, numpy, pandas, etc..\\n\\n# Available Tools (can be empty):\\nEach Class tool is described in JSON format. When you call a tool, import the tool first.\\n{}\\n\\n# Constraints:\\n- Ensure the output new code is executable in the same Jupyter notebook with previous tasks code have been executed.\\n- Always prioritize using pre-defined tools for the same functionality.\\n\"}]": {
-        "code": "import matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Set the style of seaborn\nsns.set_style('whitegrid')\n\n# Pairplot to visualize the relationships between features\nsns.pairplot(iris_df, hue='target_names', markers='+')\nplt.show()"
-    },
     "\n## context\n\n\n-----\n\n## format example\n[CONTENT]\n{\n    \"invoice\": \"False\"\n}\n[/CONTENT]\n\n## nodes: \"<node>: <type>  # <instruction>\"\n- invoice: <class 'bool'>  # if it's a invoice file, return True else False\n\n\n## constraint\nLanguage: Please use the same language as Human INPUT.\nFormat: output wrapped inside [CONTENT][/CONTENT] like format example, nothing else.\n\n## action\nFollow instructions of nodes, generate output and make sure it follows the format example.\n": "[CONTENT]\n{\n    \"invoice\": \"True\"\n}\n[/CONTENT]"
 }

tests/metagpt/provider/test_openai.py

@@ -1,5 +1,3 @@
-import json
-
 import pytest
 from openai.types.chat import (
     ChatCompletion,
@@ -106,9 +104,11 @@ class TestOpenAI:
 
     def test_aask_code_json_decode_error(self, json_decode_error):
         instance = OpenAILLM(mock_llm_config)
-        with pytest.raises(json.decoder.JSONDecodeError) as e:
-            instance.get_choice_function_arguments(json_decode_error)
-        assert "JSONDecodeError" in str(e)
+        code = instance.get_choice_function_arguments(json_decode_error)
+        assert "code" in code
+        assert "language" in code
+        assert "hello world" in code["code"]
+        logger.info(f'code is : {code["code"]}')
 
 
 @pytest.mark.asyncio

tests/metagpt/tools/libs/test_email_login.py

@@ -0,0 +1,7 @@
+from metagpt.tools.libs.email_login import email_login_imap
+
+
+def test_email_login(mocker):
+    mock_mailbox = mocker.patch("metagpt.tools.libs.email_login.MailBox.login")
+    mock_mailbox.login.return_value = mocker.Mock()
+    email_login_imap("test@outlook.com", "test_password")